Stomatal Conductance Model Research Articles

LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism

Abstract. Land biosphere processes are of central importance to the climate system. Specifically, ecosystems interact with the atmosphere through a variety of feedback loops that modulate energy, water, and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land–atmosphere interactions. Dynamic global vegetation models (DGVMs) attempt to capture land ecosystem processes and are increasingly incorporated into Earth system models (ESMs), which makes it possible to study the coupled dynamics of the land biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water, and CO2 between the land ecosystem and the atmosphere and thus provide surface boundary conditions to an atmospheric model over land. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modelled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types. We find that the emerging ecosystem composition and carbon fluxes are sensitive to both the choice of stomatal conductance model and the response of plant water uptake to soil moisture. The new implementation described in this work lays the foundation for using the well-established LPJ-GUESS DGVM as an alternative land surface model (LSM) in coupled land–biosphere–climate studies, where an accurate representation of ecosystem processes is essential.

Evapotranspiration Partitioning Based on Leaf and Ecosystem Water Use Efficiency

AbstractPartitioning evapotranspiration (ET) into evaporation (E) and transpiration (T) is essential for understanding the global hydrological cycle and improving water resource management. However, ecosystem‐level ET partitioning remains challenging. Here we proposed a novel ET partitioning method that uses the unified stomatal conductance model to estimate T:ET by calculating the ratio of the ecosystem water use efficiency (WUEeco) to leaf WUE (WUEleaf) using half‐hourly flux data. The WUEleaf values estimated by the unified stomatal conductance model agree with an independently measured ratio of hourly photosynthetic rate to T rate (R2 = 0.69). The sensitivity of T:ET to the key parameter g1 varied among different plant functional types (PFTs), but the T:ET variations for each PFT were all controlled within 20% when g1 altered within its 95% confidence interval. The mean annual T:ET was highest for evergreen broadleaf forests (0.63), followed by deciduous broad forests (0.62), grasslands (0.52), evergreen needleleaf forests (0.43) and woody savannas (0.40). C3 croplands had higher T:ET (0.65) than C4 croplands (0.48). Seasonal variations in T:ET varied across PFTs and the leaf area index explained about 50% of the variation in seasonal T:ET. Our method is not only consistent with other three EC‐based methods: Z16, N18, and L19 (R = 0.92, 0.94, and 0.68), but also shows high correlations to sap flow‐based T (R = 0.70) at three different forest sites. The method developed in this study provides a feasible and universal approach for ET partitioning of global EC sites, improving the understanding of ecosystem T characteristics across climates and PFTs.

Machine learning and remote sensing-based modeling of the optimal stomatal behavior of crops

The slope (g1) of stomatal conductance to photosynthesis is an important parameter in the optimal stomatal behavior theory-based stomatal conductance model of Medlyn et al. (2011). Although studies have modelled the spatial variations in g1, disclosing its variations over environmental gradients and different plant functional types. However, the above methods are still not accurate enough on a global scale, as they do not consider the temporal variations in g1. To address this issue we used the Ensemble Kalman Filter (EnKF) to assimilate tower-based gross primary productivity (GPP) and latent heat flux (LE) of 17 cropland flux sites into a remote sensing (RS)-based evapotranspiration-photosynthesis coupled model, termed SCOPES-Crop, to derive the temporal variations in g1 for C3 and C4 crops. We also used the feedforward artificial neural network (FANN) along with RS variables to model g1. Results showed g1 to rise rapidly in spring and summer, and then decline in autumn. The value of g1 reached the lowest value and remained stable in wintertime. FANN-based modeling of g1 showed R(RMSE) = 0.81 (1.94 kPa0.5) and 0.90 (0.70 kPa0.5) for C3 and C4 Crops, respectively, for the testing dataset. The estimates of GPP and LE using FANN-derived g1 at the 17 flux sites were improved as compared to that using fixed g1. The mean values of site-level R(RMSE) for GPP and LE simulated using FANN-derived g1 are 0.92 (1.8 gC m−2 d-2) and 0.85 (22.5 W m−2), respectively. Our results revealed notable seasonal variations in g1, indicating the importance of considering the temporal variations in g1 in evapotranspiration-photosynthesis coupled model. The FANN along with RS variables showed great potential of representing the g1 variations.

Water status estimation of cherry trees using infrared thermal imagery coupled with supervised machine learning modeling

The implementation of artificial intelligence (AI) in parallel with remote sensing could be a powerful tool to manage irrigation scheduling on crops with narrow thresholds between water stress levels, such as cherry trees. This research assessed the water status of 'Regina' cherry trees using machine learning (ML) modeling from data extracted automatically using infrared thermal imagery (IRTI). These models were used to predict stomatal conductance (gs) and stem water potential (Ψs) (Model 1) and a complete assessment using a matrix differential analysis procedure per IRTI of cherry tree canopies' temperature and relative humidity (Model 2). Results showed that the supervised ML regression models presented high and significant correlation coefficients (R = 0.83 and R = 0.81, respectively) without signs of overfitting assessed through their performance. The complex interactions among climatic factors, the soil moisture, and canopy architecture observed in cherry trees or any other fruit tree oblige exploring the performance of ML-based models to offer simple alternatives for decision-making processes in the field.

Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)

Abstract. Stomata play a central role in regulating the exchange of carbon dioxide and water vapor between ecosystems and the atmosphere. Their function is represented in land surface models (LSMs) by conductance models. The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is a dynamic vegetation demography model that can simulate both detailed plant demographic and physiological dynamics. To evaluate the effect of stomatal conductance model formulation on forest water and carbon fluxes in FATES, we implemented an optimality-based stomatal conductance model – the Medlyn (MED) model – that simulates the relationship between photosynthesis (A) and stomatal conductance to water vapor (gsw) as an alternative to the FATES default Ball–Woodrow–Berry (BWB) model. To evaluate how the behavior of FATES is affected by stomatal model choice, we conducted a model sensitivity analysis to explore the response of gsw to climate forcing, including atmospheric CO2 concentration, air temperature, radiation, and vapor pressure deficit in the air (VPDa). We found that modeled gsw values varied greatly between the BWB and MED formulations due to the different default stomatal slope parameters (g1). After harmonizing g1 and holding the stomatal intercept parameter (g0) constant for both model formulations, we found that the divergence in modeled gsw was limited to conditions when the VPDa exceeded 1.5 kPa. We then evaluated model simulation results against measurements from a wet evergreen forest in Panama. Results showed that both the MED and BWB model formulations were able to capture the magnitude and diurnal changes of measured gsw and A but underestimated both by about 30 % when the soil was predicted to be very dry. Comparison of modeled soil water content from FATES to a reanalysis product showed that FATES captured soil drying well, but translation of drying soil to modeled physiology reduced the models' ability to match observations. Our study suggests that the parameterization of stomatal conductance models and current model response to drought are the critical areas for improving model simulation of CO2 and water fluxes in tropical forests.

Temporal variation in stomatal sensitivity to vapour pressure deficit in western riparian forests

Abstract Increasing atmospheric vapour pressure deficit (D) can influence plant water and carbon uptake. However, growing season variation in stomatal responses to D among tree taxa has not been thoroughly quantified and therefore has not been well‐characterized in stomatal regulation models. Using sap flux data from nine riparian species spanning a 600‐m elevation gradient in semi‐arid northern Utah, USA, we fit a time‐varying empirical model of stomatal conductance to D in a hierarchical Bayesian framework. The reference conductance (Gref, conductance at D = 1 kPa) term was modelled as a function of cumulative growing season D, which varied with site elevation. Seven species exhibited Gref that varied significantly with cumulative growing season D, but the direction was not consistent among species. Two low‐elevation ring‐porous species, the invasive Tamarix ramosissima and Elaeagnus angustifolia, exhibited significantly positive correlation between Gref and cumulative D, such that standardized stomatal sensitivity (S) decreased during the season. Despite lower D at the mid‐ and high‐elevation sites, five diffuse‐porous native species exhibited progressively increasing sensitivity to D during the growing season. Stomatal strategies exhibit seasonal trends that vary by environmental conditions (D) and functional traits (wood anatomy), which complicates the prediction of plant hydraulic function under increasing atmospheric drought. In the increasingly arid western United States, the progressively less sensitive stomatal behaviour of invasive taxa may hasten their dominance in riparian forests. Read the free Plain Language Summary for this article on the Journal blog.

Leaf relative uptake of carbonyl sulfide to CO2 seen through the lens of stomatal conductance-photosynthesis coupling.

Carbonyl sulfide (COS) has emerged as a multi-scale tracer for terrestrial photosynthesis. To infer ecosystem-scale photosynthesis from COS fluxes often requires knowledge of leaf relative uptake (LRU), the concentration-normalized ratio between leaf COS uptake and photosynthesis. However, current mechanistic understanding of LRU variability remains inadequate for deriving robust COS-based estimates of photosynthesis. We derive a set of closed-form equations to describe LRU responses to light, humidity and CO2 based on the Ball-Berry stomatal conductance model and the biochemical model of photosynthesis. This framework reproduces observed LRU responses: decreasing LRU with increasing light or decreasing humidity; it also predicts that LRU increases with ambient CO2 . By fitting the LRU equations to flux measurements on a C3 reed (Typha latifolia), we obtain physiological parameters that control LRU variability, including an estimate of the Ball-Berry slope of 7.1 without using transpiration measurements. Sensitivity tests reveal that LRU is more sensitive to photosynthetic capacity than to the Ball-Berry slope, indicating stomatal response to photosynthesis. This study presents a simple framework for interpreting observed LRU variability and upscaling LRU. The stoma-regulated LRU response to CO2 suggests that COS may offer a unique window into long-term stomatal acclimation to elevated CO2 .

A Whole Leaf Comparative Study of Stomatal Conductance Models.

We employed a detailed whole leaf hydraulic model to study the local operation of three stomatal conductance models distributed on the scale of a whole leaf. We quantified the behavior of these models by examining the leaf-area distributions of photosynthesis, transpiration, stomatal conductance, and guard cell turgor pressure. We gauged the models' local responses to changes in environmental conditions of carbon dioxide concentration, relative humidity, and light irradiance. We found that a stomatal conductance model that includes mechanical processes dependent on local variables predicts a spatial variation of physiological activity across the leaf: the leaf functions of photosynthesis and transpiration are not uniformly operative even when external conditions are uniform. The gradient pattern of hydraulic pressure which is needed to produce transpiration from the whole leaf is derived from the gradient patterns of turgor pressures of guard cells and epidermal cells and consequently leads to nonuniform spatial distribution patterns of transpiration and photosynthesis via the mechanical stomatal model. Our simulation experiments, comparing the predictions of two versions of a mechanical stomatal conductance model, suggest that leaves exhibit a more complex spatial distribution pattern of both photosynthesis and transpiration rate and more complex dependencies on environmental conditions when a non-linear relationship between the stomatal aperture and guard cell and epidermal cell turgor pressures is implemented. Our model studies offer a deeper understanding of the mechanism of stomatal conductance and point to possible future experimental measurements seeking to quantify the spatial distributions of several physiological activities taking place over a whole leaf.

Quantifying Agricultural Drought Severity for Spring Wheat Based on Response of Leaf Photosynthetic Features to Progressive Soil Drying

Agricultural drought definition focuses on the effect of water deficit during the crop growth period on the final crop yield. However, it is difficult to quantify the dynamic process for agricultural drought precisely during the crop growing season and then relate its impact to the final crop yield. This study was conducted to quantify agricultural drought severity for spring wheat (Triticum aestivum L.) at the jointing stage based on the response of leaf physiological parameters to progressive soil drying. The leaf potential and gas exchange parameters were observed daily using a DewPoint Potential Meter (WP4) and portable photosynthetic apparatus (LI-6400) at the jointing stage of spring wheat for two different water treatments: well water supply and natural drought, respectively. The results showed that the leaf photosynthetic features’ response to available soil water could be classified into five main stages, as the available soil water thresholds were at 0.41, 0.2, 0.12, and 0.04, respectively. We defined those five stages as no agricultural drought, mild agricultural drought, moderate agricultural drought, severe agricultural drought, and extremely severe agricultural drought based on the different mechanisms of the net photosynthesis rate’s response to progressive soil drying. The parameters of three stomatal conductance models, i.e., Ball–Berry, Leuning, and Medlyn, had two apparently different groups of values divided by moderate agricultural drought. This study combined atmosphere–soil–crop as a unit to quantify agricultural drought severity during the crop growth period could be used to model crop growth and development under water deficit conditions and calculate agricultural drought indices in drought research and management.

Comparison of several models for estimating gross primary production of drip-irrigated maize in arid regions

Gross primary productivity (GPP) is the total amount of carbon absorbed by green plants through photosynthesis. As an important part of the global carbon cycle, photosynthesis is the process of converting solar energy into chemical energy. Therefore, accurate estimation of GPP is important for understanding the regional and global carbon flux variations. Based on the flux data of spring maize under drip irrigation in northwest China during 2014∼2018, this study compared and evaluated the simulation accuracy of several models for estimating GPP under different growing seasons and environmental conditions, and optimized the rectangular hyperbolic (RH) model. The results showed that none of these models consistently had the highest simulation accuracy over a 5-year period. The total GPP simulated by the light response photosynthetic models was closer to the observed value than the light use efficiency (LUE) model and stomatal conductance models. Under different growing stages and environmental conditions, the simulation accuracy of different models was different: (1) RH model had the best simulation effect when the maize was completely covered (i.e. LAI>2), but stomatal conductance-photosynthesis-transpiration coupling (SMPT-SB) model had the best simulation effect in the early growing stage; (2) when the soil water content was high, RH model has the best simulation effect. On the contrary, the simulation effect of the non-rectangular hyperbolic (N-RH) model was slightly better than other models; (3) the LUE model has a strong dependence on light conditions. Therefore, when Rn≤100, all the other 8 models except the LUE model could well simulate GPP. When Rn>100, the RH model had the best simulation effect; (4) when VPD≤1.5, the simulation accuracy of RH model was the highest; when VPD>1.5, SMPT-SB had the best simulation effect. To obtain parameters in the models more quickly and accurately, we improved the RH model by establishing the relationship between maximum photosynthetic rate (Pmax) and leaf area index (LAI), initial quantum efficiency (α) and LAI, and obtained the RH-LAI model. Compared with the RH model, the simulation accuracy of the improved RH-LAI model was improved. And the reliability of the RH-LAI model was verified in other sites. Therefore, the research results showed that in the regions with fewer observation indexes, we can use the RH-LAI model with a simple structure and convenient calculation to calculate the daily GPP by integrating it on the time scale.

Characteristic of Stomatal Conductance and Optimal Stomatal Behaviour in an Arid Oasis of Northwestern China

Stomatal conductance (gs), the process that governs plant carbon uptake and water loss, is fundamental to most Land Surface Models (LSMs). With global change accelerating, more attention should be paid to investigating stomatal behavior, especially in extremely arid areas. In this study, gas exchange measurements and environmental/biological variables observations during growing seasons in 2016 and 2017 were combined to investigate diurnal and seasonal characteristics of gs and the applicability of the optimal stomatal conductance model in a desert oasis vineyard. The results showed that the responses of gs to environmental factors (photosynthesis active radiation, PAR; vapor pressure deficit, VPD; and temperature, T) formed hysteresis loops in the daytime. The stomatal conductance slope, g1, a parameter in the unified stomatal optimal model, varied in different growing seasons and correlated with the soil-to-leaf hydraulic conductance (KL). These results indicated the potential bias when using a constant g1 value to simulate gs and highlighted that the water-use strategy of oasis plants might not be consistent throughout the entire growing season. Our findings further help to achieve a better understanding of stomata behavior in responding to climate change and encourage future efforts toward a more accurate parameterization of gs to improve the modeling of LSMs.

黄土高原油松冠层气孔导度和蒸腾变化特征与模拟

PDF HTML阅读 XML下载 导出引用 引用提醒 黄土高原油松冠层气孔导度和蒸腾变化特征与模拟 DOI: 10.5846/stxb202106041484 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0501603） Characteristics and modeling of canopy stomatal conductance and transpiration of Pinus tabuliformis on the Loess Plateau Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:油松是黄土高原重要的造林树种，模拟其冠层气孔导度和蒸腾对区域水量平衡计算和人工林可持续经营具有重要意义。基于2015-2018年TDP （Thermal dissipation probes）方法所测得液流数据，分析了黄土高原地区油松冠层平均气孔导度（gc）与冠层蒸腾（Tr）的变化特征与影响因素，并采用Penman-Monteith公式和Jarvis型气孔导度模型模拟了其gc和Tr的变化过程，结果表明：（1）该地区油松gc和Tr日内变化均呈现单峰型，日均蒸腾耗水量为（1.25±0.57） mm/d，生长季（4-10月）总蒸腾耗水量均值为195.47 mm。（2）gc的日内变化受太阳辐射（Rad）驱动（偏相关系数为0.65），当Rad高于300 W/m2时，驱动作用减弱；gc的日内变化受水汽压亏缺（VPD）控制（偏相关系数为-0.41），随VPD的增加而降低；gc的日际变化受土壤水分限制（偏相关系数为0.46），当根区相对有效含水率（REW）低于0.45时，限制作用明显。（3）结合Penman-Monteith公式与Jarvis型模型能有效模拟黄土高原油松gc与Tr变化，小时尺度gc与Tr模拟的纳什效率系数（NSE）分别为0.80和0.78，日尺度Tr模拟的NSE为0.76。 Abstract:Pinus tabuliformis is an important afforestation tree species on the Loess Plateau. Accurate estimation of its canopy stomatal conductance and transpiration is of great significance for regional water balance and sustainable forest management. Previous studies have simulated the daily variation of the transpiration of Pinus tabuliformis based on the response of its stomatal conductance to environmental factors, but the influence of soil moisture has not been considered. Soil moisture is one of the most important factors that limit plant transpiration in this region. Considering the response of stomatal conductance to soil moisture is crucial for modeling transpiration. To estimate the transpiration of Pinus tabulaeformis, the trunk sap flow velocity was measured by the thermal dissipation probes (TDP) method from 2015 to 2018. The meteorological factors and soil moisture were also monitored simultaneously. We analyzed the characteristics and controlling factors of the mean canopy stomatal conductance (gc) and transpiration (Tr) of Pinus tabuliformis on the Loess Plateau. Then the Penman-Monteith formula and Jarvis-type stomatal conductance model were used to simulate the process of its gc and Tr. The results showed that:(1) The daily variation of gc and Tr displayed a unimodal curve. The average daily transpiration rate of Pinus tabuliformis was (1.25±0.57) mm/d, and the mean annual total water consumption in the growing season (April to October) was 195.47 mm. (2) Solar radiation (Rad) was the main driving factor of the diurnal variation of gc (the partial correlation coefficient was 0.65), while the driving effect was weakened when the Rad was higher than 300 W/m2. Vapor pressure deficit (VPD) was the main controlling factor of gc diurnal variation (the partial correlation coefficient was -0.41) and gc decreased with the increase of VPD. Soil moisture was the main limiting factor of the daily variation of gc (the partial correlation coefficient was -0.46). When the relative extractable water content (REW) of the root zone was lower than 0.42, gc would decrease rapidly with the decrease of soil moisture. (3) Combining the Penman-Monteith formula and Jarvis-type model could accurately simulate the variation of gc and Tr. The Nash-Sutcliffe efficiency coefficient (NSE) of hourly gc and Tr simulations were 0.80 and 0.78, respectively, while the NSE of daily Tr simulation was 0.76. The parameters of the model obtained in this study were consistent with the basic characteristics of drought tolerance of Pinus tabuliformis, and could provide an important reference for evaluating the canopy stomatal conductance and transpiration variation of the Pinus tabuliformis on the Loess Plateau. 参考文献 相似文献 引证文献

Effects of elevated CO2 and temperature on soybean growth and gas exchange rates: A modified GLYCIM model

GLYCIM is a comprehensive soybean crop simulator that simulates soil, plant, and atmospheric interactions at the physical and physiological process level. The current research focused on replacing the original photosynthesis equation with a leaf-level coupled energy balance model of photosynthesis, transpiration, and stomatal conductance to more accurately simulate responses to climate change. Soybean gas exchange and growth data from six SPAR (Soil–Plant–Atmosphere Research) chamber experiments at four temperature treatments (24/18 °C, 28/22 °C, 32/26 °C, and 36/30 °C, respectively) under ambient (457 µmol mol−1) CO2 and two temperature treatments (28/22 °C and 32/26 °C, respectively) under elevated (670 µmol mol−1) CO2 were used to evaluate the ability of two GLYCIM model versions, original and modified, for simulating the responses of canopy photosynthesis, transpiration rates and observed harvest data. Simulation results showed that the modified GLYCIM had significantly better agreement with observed canopy photosynthetic and transpiration rates, as exhibited by higher values of IA (seasonal photosynthesis, average 0.86 versus 0.82; transpiration, average 0.95 versus 0.84) and lower values of RMSE (seasonal photosynthesis, average 0.58 versus 0.64 mol CO2 m−2 season−1; transpiration, average 1.99 versus 3.16 mmol H2O m−2 s−1) when compared to the original GLYCIM. The simulation of carbon partitioning to stems, leaves, pod and seed was reasonably accurate for both versions, although leaf and stem growth were under-predicted at warmest temperature treatments at either CO2 level. Overall, the modified GLYCIM provided an improved fit of gas exchange and dry matter over a broad range of temperatures and CO2 levels. Given additional evaluation with field data, these improvements will be useful to study adaptation responses for projected warmer, CO2 enriched, climate.

Specification of Modified Jarvis Model Parameterization for Pinus cembra

The high ambient ozone concentrations cause impairing effects on vegetation leading to plant injuries. The potential ozone uptake to vegetation through open stomata can be quantified using stomatal conductance measurements under the local environmental conditions. This study compares the ozone stomatal conductance to vegetation obtained with a modified Jarvis formula adopted from the Vegetation Manual of United Nations Economic Commission for Europe, and experimental field measurements’ data. The stomatal conductance was measured by a portable photosynthesis and gas exchange analyzer system LiCOR6400. The measurements were performed in the submontane environment of the High Tatra Mountains in Slovakia on Swiss pine (Pinus cembra), as a native species of the local flora. According to previous studies, Swiss pine is considered as an ozone-sensitive species. The modified Jarvis model for the ozone stomatal conductance is compared with the field measurements. The suitable parameterization of the modified Jarvis model for Swiss pine is obtained. The parameterization of stomatal conductance for Swiss pine in the local environment would help understand its specificity and similarity to other conifer species. In the case of using parameterization for a boreal coniferous from the Vegetation Manual of the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, validation of the model with the measurements without temperature adjustment of the conifer chamber achieved a coefficient of determination of R2=0.75. This result is not in contradiction with the previous researches. With the optimal set of parameters, obtained in this paper, the Jarvis model reaches R2=0.85. The data suggest that Jarvis-type models with appropriate parameterization are applicable for stomatal conductance estimation for Pinus cembra when the measurements do not modify the temperature regime.

Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes

Stomatal closure decreases water loss and is one of the main mechanisms that trees can use to mitigate drought-induced physiological stress. The adaptability of trees to drought is likely to be of increasing importance as climate changes occur around the world. Modelling stomatal regulation can help improve our understanding of how forests respond to their environment. Traditionally, empirical models have been used to model stomatal responses, however these models cannot always capture nonlinear responses and their parameters are often difficult to measure. In this study various machine learning (ML) models were able to capture stomatal responses of multiple tree species. We showed that ML can be a useful tool for predicting stomatal response based on climate variables and species traits. A random forest model performed the best with an R2 of 75 %, compared to the empirical Ball-Berry stomatal conductance model (BWB) (R2 = 41 %). In this study, the use of a combined dataset consisting out of data from multiple studies were successfully used, showcasing the use of data across studies. This also allowed for an ML model to be trained on 36 tree species from 5 forest biomes, from measurements taken across 6 continents, instead of being limited to one species, increasing the versatility of the model. The importance of species-specific stomatal responses was highlighted, with the drought strategies used by plants significantly influencing stomatal responses and predictions. ML models were able to capture these trends parsimoniously without prior knowledge of the underlying physiology of the tree species. The quality of combined datasets are however still not desirable, and long-term data collection using standardized measuring protocols are required to increase the strength of ML models. Data taken across different climatic regions and vegetation types can also help improve the adaptability of ML models. Regardless of limitations on data accessibility, ML shows promise in modelling plant responses to changes in climate. Focus on the use of ML together with traditional models can help give further insight into various ecological mechanisms.

A comparative study of three stomatal conductance models for estimating evapotranspiration in a dune ecosystem in a semi-arid region

The accurate simulation of stomatal conductance is crucial for not only revealing the carbon and water cycle processes of an ecosystem, but also to improve the accuracy of simulations of evapotranspiration (ET). This study coupled three stomatal conductance models, i.e. the Stannard (ST), Jarvis-Stewart (JS), and Ball-Berry (BB) models, with the Shuttleworth-Wallace (SW) model to estimate ET for a mobile dune ecosystem in the Horqin Sandy Land, North China. These models were calibrated and validated using eddy covariance (EC) measurements taken during the growing season between 2013 and 2018. The results indicated that the SW-BB model showed better performance in comparison to the SW-JS and SW-ST models at half-hourly and daily timescales. The stomatal conductance models incorporating soil moisture (SM) content generally showed better performance during the extreme drought period, with the rank of the three models according to performance being: SW-BB > SW-JS > SW-ST. The models showed the highest sensitivity to SM when incorporating the effect of SM on stomatal conductance, indicating that SM has an important effect on stomatal conductance and ET. The results of this study indicate that of the models assessed, the Ball-Berry stomatal conductance model coupled with the SW model is optimal for estimating ET in dune ecosystems with sparse vegetation.

On the Treatment of Soil Water Stress in GCM Simulations of Vegetation Physiology

Current land surface schemes in weather and climate models make use of the so-called coupled photosynthesis–stomatal conductance (A–gs) models of plant function to determine the surface fluxes that govern the terrestrial energy, water and carbon budgets. Plant physiology is controlled by many environmental factors, and a number of complex feedbacks are involved, but soil moisture control on root water uptake is primary, particularly in sub-tropical to temperate ecosystems. Land surface models represent plant water stress in different ways, but most implement a water stress factor, β, which ranges linearly (more recently also curvilinearly) between β = 1 for unstressed vegetation and β = 0 at the wilting point, expressed in terms of volumetric water content (θ). β is most commonly used to either limit A or gs, and hence carbon and water fluxes, and a pertinent research question is whether these treatments are in fact interchangeable. Following Egea et al. (Agricultural and Forest Meteorology, 2011, 151 (10), 1,370–1,384) and Verhoef et al. (Agricultural and Forest Meteorology, 2014, 191, 22–32), we have implemented new β treatments, reflecting higher levels of biophysical complexity in a state-of-the-art LSM, Joint UK Land Environment Simulator, by allowing root zone soil moisture to limit plant function non-linearly and via individual routes (carbon assimilation, stomatal conductance, or mesophyll conductance) as well as any (non-linear) combinations thereof. The treatment of β does matter to the prediction of water and carbon fluxes: this study demonstrates that it represents a key structural uncertainty in contemporary LSMs, in terms of predictions of gross primary productivity, energy fluxes and soil moisture evolution, both in terms of climate means and response to a number of European droughts, including the 2003 heat wave. Treatments allowing ß to act on vegetation fluxes via stomatal and mesophyll routes are able to simulate the spatiotemporal variability in water use efficiency with higher fidelity during the growing season; they also support a broader range of ecosystem responses, e.g., those observed in regions that are radiation limited or water limited. We conclude that current practice in weather and climate modelling is inconsistent, as well as too simplistic, failing to credibly simulate vegetation response to soil water stress across the typical range of variability that is encountered for current European weather and climate conditions, including extremes of land surface temperature and soil moisture drought. A generalized approach performs better in current climate conditions and promises to be, based on responses to recently observed extremes, more trustworthy for predicting the impacts of climate change.

Long-term estimation of the canopy photosynthesis of a leafy vegetable based on greenhouse climate conditions and nadir photographs

In horticultural crop production, accurately estimating the canopy photosynthetic rate (Ac) based on greenhouse climate conditions is of practical value for predicting and controlling crop growth and yield. We developed a method for continuously estimating Ac in a greenhouse from readily obtainable information (i.e., climatic data and canopy photographs) by combining a canopy photosynthesis model with image analysis. The canopy photosynthesis model was based on a sun/shade representation of the crop canopy combined with models of single-leaf photosynthesis, stomatal conductance, mass transfer and leaf energy balance. This combination allowed 1) the incorporation of all major climatic variables (i.e., radiation, CO2 concentration, air temperature, humidity, and wind velocity) into the Ac estimation and 2) the simultaneous estimation of Ac, canopy transpiration rate (Ec), and leaf temperature (TL). The leaf area index (Lc), which changes considerably throughout the growth period of a crop canopy, was evaluated nondestructively from the gap fractions of nadir digital photographs (i.e., the fractions of nonleaf area). The canopy photosynthesis model and image analysis results were combined and used to estimate the Ac, Ec, and TL of spinach canopies grown at ambient and elevated CO2 concentrations (c. 400 and 800 µmol mol−1, respectively). Regardless of the CO2 concentration, the estimates of Ac, Ec, and TL were in good agreement with measurements obtained with the open chamber method throughout the growth period. Analyzing the sensitivity of Ac to input variables revealed that the effects of climatic variables on Ac can vary considerably depending on the Lc and incoming radiation. The proposed method enables not only the estimation of Ac from readily obtainable information but also the quantitative evaluation of the effect of climate control on Ac in horticultural greenhouses.

Comparison of an improved Penman-Monteith model and SWH model for estimating evapotranspiration in a meadow wetland in a semiarid region

Accurately estimating evapotranspiration (ET) for meadow wetland ecosystems is of great significance for water management in semiarid regions. Taking a meadow wetland ecosystem in the Horqin Sandy Land as an example, this study coupled the Ball-Berry canopy stomatal conductance model to Penman-Monteith (P-M) model and Shuttleworth-Wallace (S-W) model, and the improved P-M model and SWH model were calibrated and validated by long-term (2013–2018 growing seasons) eddy covariance (EC) measurements. The results indicated that the improved P-M model and SWH model performed well either at half-hourly or daily timescales, with high coefficient of determination (R2) and index of agreement (IA) and low root mean square error (RMSE). Generally, the SWH model performed better than the improved P-M model, especially under the low leaf area index (LAI) conditions. In addition, in the growing seasons from 2013 to 2018, the mean ET measured by the EC system was 2.78 mm/d, which was approximately equal to the mean modeled ET from the SWH model (2.75 mm/d) and slightly higher than the mean modeled ET from the improved P-M model (2.34 mm/d). The improved P-M model and SWH model were highly sensitive to the parameter in estimating canopy surface resistance and to vapor pressure deficit (VPD) in meteorological variables.

Incorporating cultivar-specific stomatal traits into stomatal conductance models improves the estimation of evapotranspiration enhancing greenhouse climate management

The effect of considering cultivar differences in stomatal conductance (g s ) on relative air humidity (RH)-related energy demand was addressed. We conducted six experiments in order to study the variation in evapotranspiration (ET c ) of six pot rose cultivars, investigate the underlying processes and parameterise a g s -based ET c model. Several levels of crop ET c were realised by adjusting the growth environment. The commonly applied Ball–Woodrow–Berry g s -sub-model (BWB-model) in ET c models was validated under greenhouse conditions, and showed a close agreement between simulated and measured ET c . The validated model was incorporated into a greenhouse simulator. A scenario simulation study showed that selecting low-g s cultivars reduces energy demand (≤5.75%), depending on the RH set point. However, the BWB-model showed poor prediction quality at RH lower than 60% and a good fit at higher RH. Therefore, an attempt was made to improve model prediction: the in situ -obtained data were employed to adapt and extend either the BWB-model, or the Liu-extension with substrate water potential (Ψ; BWB-Liu-model). Both models were extended with stomatal density (D s ) or pore area. Although the modified BWB-Liu-model (considering D s ) allowed higher accuracy (R 2 = 0.59), as compared to the basic version (R 2 = 0.31), the typical lack of Ψ prediction in greenhouse models may be problematic for implementation into real-time climate control. The current study lays the basis for the development of cultivar specific cultivation strategies as well as improving the g s sub-model for dynamic climate conditions under low RH using model-based control systems. • Relative air humidity control is a key but costly aspect of greenhouse climate. • Accurate estimation of evapotranspiration (ET c ) is essential for relative air humidity regulation. • Six pot rose cultivars and several cultivation regimes were used. • ET c estimation was improved using cultivar-specific stomatal conductance parameters. • The use of low-stomatal conductance cultivars secures energy saving (2.5–5.75%).